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Early postnatal striatal gene expression networks in rat: relevance to schizophrenia

2012· article· en· W3173170839 on OpenAlexaff
Gabriela Novak, Theresa Fan, Susan R. George

Bibliographic record

VenueThe FASEB Journal · 2012
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicReceptor Mechanisms and Signaling
Canadian institutionsUniversity of TorontoCentre for Addiction and Mental Health
Fundersnot available
KeywordsStriatumOffspringSchizophrenia (object-oriented programming)Prefrontal cortexNeuroscienceGene expressionBiologyDopamine receptor D2Downregulation and upregulationPhenotypeDopamineGeneEndocrinologyPsychologyPregnancyGeneticsPsychiatryCognition

Abstract

fetched live from OpenAlex

Abnormal striatal development is thought to play an important role in a number of diseases, including schizophrenia. In humans, stress during the second trimester of pregnancy leads to increased risk for schizoaffective disorders in the offspring. This is period of active development of the striatum and corresponds to the second postnatal week of striatal development in rat. We show that during the first two postnatal weeks in rat an entire gene expression network becomes down‐regulated and replaced by a mature gene expression network. Using subtractive hybridization, we identified 31 additional genes involved in this process, including 12 novel transcripts with strict developmental expression. We show that in an established schizophrenia model in rat, this developmental process is altered and results in overexpression of the dopamine 2 receptor (D2R) during the second postnatal week, with no change in expression of D1R or other genes. This may play an important role in the development of a schizophrenia‐like phenotype, as striatal D2R upregulation has been shown to cause dysregulation of the prefrontal cortex.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.010
GPT teacher head0.232
Teacher spread0.222 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2012
Admission routes1
Has abstractyes

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